US10354759B2 - Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions - Google Patents
Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions Download PDFInfo
- Publication number
- US10354759B2 US10354759B2 US15/172,742 US201615172742A US10354759B2 US 10354759 B2 US10354759 B2 US 10354759B2 US 201615172742 A US201615172742 A US 201615172742A US 10354759 B2 US10354759 B2 US 10354759B2
- Authority
- US
- United States
- Prior art keywords
- patient
- state
- physiological state
- model
- imaging procedure
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 210000004204 blood vessel Anatomy 0.000 title claims abstract description 41
- 230000017531 blood circulation Effects 0.000 claims abstract description 49
- 230000035790 physiological processes and functions Effects 0.000 claims abstract description 44
- 230000036772 blood pressure Effects 0.000 claims abstract description 27
- 238000004088 simulation Methods 0.000 claims description 44
- 238000003384 imaging method Methods 0.000 claims description 32
- 238000011282 treatment Methods 0.000 claims description 32
- 210000003484 anatomy Anatomy 0.000 claims description 15
- 230000000284 resting effect Effects 0.000 claims description 15
- 230000000544 hyperemic effect Effects 0.000 claims description 11
- 238000005259 measurement Methods 0.000 claims description 5
- 238000013500 data storage Methods 0.000 claims description 2
- 210000001367 artery Anatomy 0.000 description 19
- 229940079593 drug Drugs 0.000 description 16
- 239000003814 drug Substances 0.000 description 16
- 150000002823 nitrates Chemical class 0.000 description 15
- 230000008859 change Effects 0.000 description 14
- 210000004351 coronary vessel Anatomy 0.000 description 13
- 230000004044 response Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 10
- 239000000463 material Substances 0.000 description 10
- OIRDTQYFTABQOQ-KQYNXXCUSA-N adenosine Chemical compound C1=NC=2C(N)=NC=NC=2N1[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O OIRDTQYFTABQOQ-KQYNXXCUSA-N 0.000 description 8
- 239000002876 beta blocker Substances 0.000 description 8
- 229940097320 beta blocking agent Drugs 0.000 description 8
- 230000003902 lesion Effects 0.000 description 7
- 238000002399 angioplasty Methods 0.000 description 6
- 230000001965 increasing effect Effects 0.000 description 6
- 230000002792 vascular Effects 0.000 description 6
- 208000031481 Pathologic Constriction Diseases 0.000 description 5
- 238000002583 angiography Methods 0.000 description 5
- 230000008321 arterial blood flow Effects 0.000 description 5
- 238000002591 computed tomography Methods 0.000 description 5
- 201000010099 disease Diseases 0.000 description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 5
- 230000036262 stenosis Effects 0.000 description 5
- 208000037804 stenosis Diseases 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- 239000002126 C01EB10 - Adenosine Substances 0.000 description 4
- 206010020565 Hyperaemia Diseases 0.000 description 4
- 229960005305 adenosine Drugs 0.000 description 4
- 238000013459 approach Methods 0.000 description 4
- 230000000747 cardiac effect Effects 0.000 description 4
- 208000029078 coronary artery disease Diseases 0.000 description 4
- 238000010801 machine learning Methods 0.000 description 4
- XQYZDYMELSJDRZ-UHFFFAOYSA-N papaverine Chemical compound C1=C(OC)C(OC)=CC=C1CC1=NC=CC2=CC(OC)=C(OC)C=C12 XQYZDYMELSJDRZ-UHFFFAOYSA-N 0.000 description 4
- 238000013146 percutaneous coronary intervention Methods 0.000 description 4
- 238000001356 surgical procedure Methods 0.000 description 4
- ZKHQWZAMYRWXGA-KQYNXXCUSA-J ATP(4-) Chemical compound C1=NC=2C(N)=NC=NC=2N1[C@@H]1O[C@H](COP([O-])(=O)OP([O-])(=O)OP([O-])([O-])=O)[C@@H](O)[C@H]1O ZKHQWZAMYRWXGA-KQYNXXCUSA-J 0.000 description 3
- ZKHQWZAMYRWXGA-UHFFFAOYSA-N Adenosine triphosphate Natural products C1=NC=2C(N)=NC=NC=2N1C1OC(COP(O)(=O)OP(O)(=O)OP(O)(O)=O)C(O)C1O ZKHQWZAMYRWXGA-UHFFFAOYSA-N 0.000 description 3
- 206010008479 Chest Pain Diseases 0.000 description 3
- 230000000052 comparative effect Effects 0.000 description 3
- 238000005094 computer simulation Methods 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 238000002203 pretreatment Methods 0.000 description 3
- 229930008281 A03AD01 - Papaverine Natural products 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 2
- 201000000057 Coronary Stenosis Diseases 0.000 description 2
- 206010011089 Coronary artery stenosis Diseases 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 239000000090 biomarker Substances 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 230000010339 dilation Effects 0.000 description 2
- 238000002592 echocardiography Methods 0.000 description 2
- 230000002526 effect on cardiovascular system Effects 0.000 description 2
- 208000010125 myocardial infarction Diseases 0.000 description 2
- 210000004165 myocardium Anatomy 0.000 description 2
- 229960001789 papaverine Drugs 0.000 description 2
- 238000002600 positron emission tomography Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000002441 reversible effect Effects 0.000 description 2
- 210000002460 smooth muscle Anatomy 0.000 description 2
- 210000000329 smooth muscle myocyte Anatomy 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 210000001519 tissue Anatomy 0.000 description 2
- 230000002227 vasoactive effect Effects 0.000 description 2
- 206010002388 Angina unstable Diseases 0.000 description 1
- 200000000007 Arterial disease Diseases 0.000 description 1
- 208000037260 Atherosclerotic Plaque Diseases 0.000 description 1
- 238000012935 Averaging Methods 0.000 description 1
- 102000008186 Collagen Human genes 0.000 description 1
- 108010035532 Collagen Proteins 0.000 description 1
- 102000016942 Elastin Human genes 0.000 description 1
- 108010014258 Elastin Proteins 0.000 description 1
- 229910002651 NO3 Inorganic materials 0.000 description 1
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 description 1
- 208000007718 Stable Angina Diseases 0.000 description 1
- 208000007814 Unstable Angina Diseases 0.000 description 1
- 206010047139 Vasoconstriction Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 229960001456 adenosine triphosphate Drugs 0.000 description 1
- 230000003143 atherosclerotic effect Effects 0.000 description 1
- 238000009534 blood test Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000002490 cerebral effect Effects 0.000 description 1
- 229920001436 collagen Polymers 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 239000002872 contrast media Substances 0.000 description 1
- 238000002586 coronary angiography Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000002059 diagnostic imaging Methods 0.000 description 1
- 230000037213 diet Effects 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000000916 dilatatory effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005489 elastic deformation Effects 0.000 description 1
- 229920002549 elastin Polymers 0.000 description 1
- 238000002565 electrocardiography Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000001747 exhibiting effect Effects 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000000004 hemodynamic effect Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 238000001802 infusion Methods 0.000 description 1
- 201000004332 intermediate coronary syndrome Diseases 0.000 description 1
- 238000001990 intravenous administration Methods 0.000 description 1
- 210000003141 lower extremity Anatomy 0.000 description 1
- 238000002483 medication Methods 0.000 description 1
- 230000004066 metabolic change Effects 0.000 description 1
- 230000004089 microcirculation Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000010412 perfusion Effects 0.000 description 1
- 230000037081 physical activity Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 229960003614 regadenoson Drugs 0.000 description 1
- LZPZPHGJDAGEJZ-AKAIJSEGSA-N regadenoson Chemical compound C1=C(C(=O)NC)C=NN1C1=NC(N)=C(N=CN2[C@H]3[C@@H]([C@H](O)[C@@H](CO)O3)O)C2=N1 LZPZPHGJDAGEJZ-AKAIJSEGSA-N 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000011144 upstream manufacturing Methods 0.000 description 1
- 230000025033 vasoconstriction Effects 0.000 description 1
- 229940124549 vasodilator Drugs 0.000 description 1
- 239000003071 vasodilator agent Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- G06F17/5009—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
Definitions
- Various embodiments of the present disclosure relate generally to medical modeling and related methods. More specifically, particular embodiments of the present disclosure relate to systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions resulting from changes in blood flow or pressure.
- Coronary artery disease may cause the blood vessels providing blood to the heart to develop lesions, such as a stenosis (abnormal narrowing of a blood vessel). As a result, blood flow to the heart may be restricted.
- a patient suffering from coronary artery disease may experience chest pain, referred to as chronic stable angina during physical exertion or unstable angina when the patient is at rest. A more severe manifestation of disease may lead to myocardial infarction, or heart attack.
- Patients suffering from chest pain and/or exhibiting symptoms of coronary artery disease may be subjected to one or more tests that may provide some indirect evidence relating to coronary lesions.
- noninvasive tests may include electrocardiograms, biomarker evaluation from blood tests, treadmill tests, echocardiography, single positron emission computed tomography (SPECT), and positron emission tomography (PET). These noninvasive tests, however, typically do not provide a direct assessment of coronary lesions or assess blood flow rates.
- SPECT single positron emission computed tomography
- PET positron emission tomography
- the noninvasive tests may provide indirect evidence of coronary lesions by looking for changes in electrical activity of the heart (e.g., using electrocardiography (ECG)), motion of the myocardium (e.g., using stress echocardiography), perfusion of the myocardium (e.g., using PET or SPECT), or metabolic changes (e.g., using biomarkers).
- ECG electrocardiography
- motion of the myocardium e.g., using stress echocardiography
- perfusion of the myocardium e.g., using PET or SPECT
- metabolic changes e.g., using biomarkers
- anatomic data may be obtained noninvasively using coronary computed tomographic angiography (CCTA).
- CCTA may be used for imaging of patients with chest pain and involves using computed tomography (CT) technology to image the heart and the coronary arteries following an intravenous infusion of a contrast agent.
- CT computed tomography
- obtaining anatomic data using CCTA often means that models based on the anatomic data reflect a patient's state as he/she is undergoing imaging (e.g., CCTA imaging). Therefore, anatomic models for assessing blood flow rates are based on patient conditions during an imaging procedure. For example, patient-specific anatomic models for simulating arterial blood flow are often obtained while a patient is in a baseline condition during imaging and prior to treatment.
- various forms of treatment may affect anatomy and consequently, blood flow.
- a patent's state may change due to any array of medical procedures and/or health conditions.
- models for assessing blood flow may fail to reflect the change in state.
- methods and systems accounting for changes in a patient's physiological state in indirect assessments of blood flow rates.
- methods and systems for creating an anatomical model based on a patient's change in state in order to improve the accuracy of a simulation performed using the model. More specifically, creating an anatomical model may entail modeling changes in patient-specific blood vessel geometry and boundary conditions.
- One method includes: determining, using a processor, a first anatomic model of one or more blood vessels of a patient; determining a biomechanical model of the one or more blood vessels based on at least the first anatomic model; determining one or more parameters associated with a physiological state of the patient; and creating a second anatomic model based on the biomechanical model and the one or more parameters associated with the physiological state.
- a system for anatomical modeling comprises: a data storage device storing instructions for anatomical modeling; and a processor configured for: determining, using a processor, a first anatomic model of one or more blood vessels of a patient; determining a biomechanical model of the one or more blood vessels based on at least the first anatomic model; determining one or more parameters associated with a physiological state of the patient; and creating a second anatomic model based on the biomechanical model and the one or more parameters associated with the physiological state.
- a non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for anatomical modeling.
- the method includes: determining, using a processor, a first anatomic model of one or more blood vessels of a patient; determining a biomechanical model of the one or more blood vessels based on at least the first anatomic model; determining one or more parameters associated with a physiological state of the patient; and creating a second anatomic model based on the biomechanical model and the one or more parameters associated with the physiological state.
- FIG. 1 is a block diagram of an exemplary system and network for modeling changes in patient-specific blood vessel geometry and boundary conditions, according to an exemplary embodiment of the present disclosure.
- FIG. 2 is a block diagram of an exemplary method of changing geometry and boundary conditions in a blood flow simulation arising from different states of a patient, according to an exemplary embodiment of the present disclosure.
- FIG. 3 is a block diagram of an exemplary method of determining a second state model of conditions, according to an exemplary embodiment of the present disclosure.
- FIG. 4 is a block diagram of an exemplary method of determining an updated geometric model based on the second state conditions, according to an exemplary embodiment of the present disclosure.
- FIG. 5 is a block diagram of an exemplary method of determining geometry responses to different physiologic conditions, according to an exemplary embodiment of the present disclosure.
- patient-specific anatomic models for simulating arterial blood flow are based on image data associated with one state.
- coronary artery anatomic data may be obtained under baseline or resting conditions.
- coronary artery anatomic data may be obtained based on an anatomic state achieved during imaging, including states that increase blood vessel size and blood flow to improve image quality.
- Geometric models may be created and boundary conditions assigned based on the image data from a baseline condition or imaging conditions. Simulations modeling reversible, physiological states (e.g., blood flow simulations associated with drugs, exercise, and/or treatment) are often performed based on the anatomic and geometric model associated with the first state. However, drugs, exercise, and/or treatment may all cause changes in blood vessel geometry and boundary conditions from the first state.
- a geometry of a patient's anatomy may change due to various conditions or treatments, including administration of drugs (e.g., adenosine or other drugs to increase blood flow), simulations of medical conditions (e.g., simulated hyperemia), simulations of physical activities or conditions (e.g., exercise), angioplasty, surgery (e.g., stenting or bypass grafting), etc. Therefore, a desire exists for patient-specific models for simulating arterial blood flow that may account for a representation of a patient's state, where the patient's state may differ from a state from which the anatomic model was built. Simulating arterial blood flow using a patient-specific model reflecting a second state may improve accuracy of simulation results.
- drugs e.g., adenosine or other drugs to increase blood flow
- simulations of medical conditions e.g., simulated hyperemia
- simulations of physical activities or conditions e.g., exercise
- angioplasty e.g., stenting or bypass grafting
- second state(s) may include reversible, physiological states.
- simulations and models based on the second state may further be applied to model possible treatments that may affect geometry (e.g., angioplasty, stenting, and/or bypass surgery).
- a geometric change to a model may be made (e.g., to model stenting), based on patient-specific models that reflect a second state.
- the following discussion outlines various scenarios where an anatomic and biomechanical model under which simulations are performed, may not accurately represent a patient's state.
- simulations may be performed using patient-specific anatomic models based on image data obtained under resting conditions. Geometric models and boundary condition models based on these baseline conditions may then be used as input to computer models in order to predict flow and pressure under a physiologic state, including during the administration of adenosine or other drugs to increase blood flow and simulate exercise, or after angioplasty and stenting or bypass grafting.
- the patient's anatomy may be at a state distinct from the first, resting state, in light of one or more treatments or conditions. Therefore, a desire exists for patient-specific models for simulating arterial blood flow to take into account a second state reflecting patient anatomy at a non-resting state.
- a patient-specific model extracted from image data may be based on a state distinct from a baseline state.
- beta blockers may be used to reduce heart rate, while nitrates may be administered to dilate large coronary arteries. Both drugs may be administered to improve image quality.
- beta blockers used to slow the heart may affect blood pressure and hence, the size of a vessel; and nitrates used during coronary computed tomography (CT) angiography may increase flow by relaxing smooth muscle cells in blood vessels, decreasing their tension (or tone), and increasing the size of the blood vessels. The increased size and flow through the vessels improves image quality.
- CT computed tomography
- the administration of beta blockers and/or nitrates may cause geometry and physiologic conditions to change to a state that may be different from a baseline state.
- the new state of the arteries from the administration of beta blockers and/or nitrates changes geometry and physiologic conditions to a state that may be different from a baseline.
- modeled changes in blood flow and pressure may cause changes in patient-specific geometric models and boundary conditions, since local vessel size may be affected by local pressure and smooth muscle tone of the vessels (which can be affected by administration of nitrates, adenosine, papaverine, adenosine triphosphate (ATP), etc.).
- an image created from baseline conditions may not account for the affect that drugs may have on anatomy geometry and boundary conditions.
- a blood flow simulation performed under the state may be expected to yield diagnostic data different from that attained prior to the administration of the drugs.
- the present disclosure is directed to a new approach including changing geometry and boundary conditions in a blood flow simulation to model an original baseline or resting state of arteries prior to administration of drugs.
- a specific example of the above embodiment may include modeling of increased flow as occurs during simulated hyperemia. Such modeling may be performed to calculated fractional flow reserve or coronary flow reserve.
- the simulations of increased flow may be typically performed based on coronary anatomic data obtained under baseline or resting conditions. In reality, the data is often obtained subsequent to administration of beta blockers and/or nitrates. More specifically, simulation of increased blood flow through coronary arteries may result in pressure changes along the coronary arteries, especially downstream of a coronary artery stenosis.
- the metric of FFR may be calculated from the ratio of downstream pressure to aortic pressure.
- blood pressure may be significantly lower at points downstream of the vessel rather than at points upstream of the vessel.
- blood pressure may be significantly lower during the hyperemic state than during the resting state.
- the blood vessels may diminish in size (i.e., “deflate”) due to the reduced pressure.
- Such changes in vessel size may affect tightness of a coronary artery stenosis or the caliber of vessels downstream from the stenosis. This in turn may affect the accuracy of the hyperemic simulation and accuracy of the predicted FFR, as compared to measured data (obtained during actual administration of vasodilators causing increased flow and pressure reduction along the length of the vessel).
- the present disclosure is directed to a new approach for changing geometry and boundary conditions in a blood flow simulation model of the hyperemic state of arteries using image data obtained without administration of drugs to increase blood flow.
- treatment recommendations may be improved with modeling taking into account changes in patient-specific blood vessel geometry and boundary conditions.
- PCI percutaneous coronary intervention
- CABG coronary artery bypass grafting
- Computer models are often used to predict changes in blood flow or pressure resulting from the treatments to aid the physician in deciding how best to treat a given patient.
- Patient-specific models for simulating PCI or CABG may be created from pre-treatment image data, then modified to incorporate a treatment plan. The modifications are generally restricted to geometric changes in diseased segments to account for dilation of stenosis with PCI or creation of an alternate conduit for blood flow with CABG.
- treatments may affect more than simply diseased segments.
- the present disclosure is further directed to a new approach for modeling geometric changes and boundary condition changes secondary to changes in blood flow or pressure resulting from treatments for arterial disease.
- the present disclosure may include updating geometric model and boundary conditions (created from pre-treatment data) to account for new post-treatment flow and pressure.
- the present disclosure may include changing geometry and boundary conditions in a blood flow simulation to model post-treatment state of arteries due to predicted changes in blood flow and pressure from models originally created using image data obtained prior to treatment.
- the present disclosure is directed to a new approach for systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions based on changes in blood flow or pressure.
- FIG. 1 depicts a block diagram of an exemplary system and network for modeling changes in patient-specific blood vessel geometry and boundary conditions.
- FIG. 1 depicts a plurality of physicians 102 and third party providers 104 , any of whom may be connected to an electronic network 100 , such as the Internet, through one or more computers, servers, and/or handheld mobile devices.
- Physicians 102 and/or third party providers 104 may create or otherwise obtain images of one or more patients' cardiac and/or vascular systems.
- the physicians 102 and/or third party providers 104 may also obtain any combination of patient-specific information, such as age, medical history, blood pressure, blood viscosity, etc.
- Physicians 102 and/or third party providers 104 may transmit the cardiac/vascular images and/or patient-specific information to server systems 106 over the electronic network 100 .
- Server systems 106 may include storage devices for storing images and data received from physicians 102 and/or third party providers 104 .
- Server systems 106 may also include processing devices for processing images and data stored in the storage devices.
- FIG. 2 is a block diagram of an exemplary method 200 of changing geometry and boundary conditions in a blood flow simulation to model a second state of a patient different from a first state of the patient (e.g., from the state in which the patient was imaged), according to an exemplary embodiment.
- the second state may be (1) a resting state, free of the administration of drugs used during imaging, (2) a hyperemic state of arteries, free of drugs used to increase blood flow, (3) a post-treatment state, or (4) any other desired state.
- step 201 may include constructing a patient-specific anatomic model.
- the model may be from two-dimensional imaging modalities (e.g., coronary angiography, biplane angiography, etc.) or three-dimensional imaging modalities (e.g., 3-D rotational angiography, coronary computed tomographic angiograph (cCTA), magnetic resonance angiography (MRA)).
- Step 201 may further include directly segmenting image data and creating a patient-specific three-dimensional anatomic model of the patient's arteries.
- step 201 may involve modifying a previously-constructed “generic” model, customizing the model for a particular patient, and creating a patient-specific model.
- step 201 may include providing, receiving, and/or loading a patient-specific anatomic model of a patient into a computer.
- the model may be from an electronic storage device (e.g., a hard drive, network drive, etc.).
- the model may represent a first, baseline state of a patient.
- the patient-specific anatomic model may include information related to arteries of interest, including the length of each segment, diameter along the length of a segment (or any other geometric description of the segment), branching patterns, presence of disease, characteristics of disease (including composition of atherosclerotic plaques), etc.
- a representation of the patient-specific model may be defined by a surface enclosing a three-dimensional volume, a one-dimensional model where the centerline of the vessels is defined together with cross-sectional area information along the length, and/or an implicit representation of a vessel surface.
- step 203 may include defining physiologic conditions associated with blood flow and pressure that reflect a patient's condition at the time that imaging was taken. Conditions at the time of imaging may make up a “first (physiological) state” for a patient.
- a patient may be administered beta blockers to lower his heart rate and/or sublingual nitrates to dilate his coronary arteries in order to improve image quality.
- Step 203 of determining physiologic conditions may include determining and/or assigning aortic pressure conditions and resistance of coronary artery microcirculation based on a patient's intake of beta blockers and/or nitrates.
- step 205 may include creating a biomechanical model of a vessel wall, for example, generating a biomechanical model for each segment of artery extracted in the patient-specific anatomic model of step 201 .
- the vessel wall model may be based on one-dimensional elastic or viscoelastic models of blood vessels. Such models may include models that typically relate pressure to vessel cross-sectional area along the length of a vessel. Exemplary models are described in Olufsen et al. (Olufsen M S. “Structured tree outflow condition for blood flow in larger systemic arteries.” Am J Physiol Heart Circ Physiol 276:H257-H268, 1999.), Wan et al. (J. Wan, B. N.
- biomechanical models of vessel wall may represent the vessel wall as a surface with spatially-varying thickness and material properties, for example, as described in Figueroa et al. (C. A. Figueroa, I. E. Vignon-Clementel, K. C. Jansen, T. J. R. Hughes, C. A. Taylor (2006) “A Coupled Momentum Method For Modeling Blood Flow In Three-Dimensional Deformable Arteries.” Computer Methods in Applied Mechanics and Engineering, Vol. 195, Issues 41-43, pp. 5685-5706.) or in Figueroa et al. (C. A. Figueroa, S. Baek, C. A. Taylor, J. D.
- a biomechanical model may include a blood vessel as a three-dimensional continuum model, as in Gee et al. (Gee M W, Förster C, Wall W A (2010) “A computational strategy for prestressing patient-specific biomechanical problems under finite deformation.” Int J Numer Methods Biomed Eng 26(1):52-72.), Gerbeau et al.
- Material properties of vessel walls may be defined based on population averaged material properties, imaging data, and/or data inferred by experimental measurement of deformation of coronary arteries during a cardiac cycle and solving an inverse optimization problem to estimate the best constitutive fit consistent with data.
- constitutive models include linear elastic, hyperelastic, linear and nonlinear viscoelastic models including those discussed in Wan et al. (J. Wan, B. N. Steele, S. A. Spicer, S. Strohband, G. R. Feijoo, T. J. R. Hughes, C. A. Taylor (2002) A One-dimensional Finite Element Method for Simulation-Based Medical Planning for Cardiovascular Disease. Computer Methods in Biomechanics and Biomedical Engineering. Vol. 5, No. 3, pp. 195-206), Raghu et al. ([R. Raghu, I. E. Vignon-Clementel, C. A. Figueroa, C. A.
- Humphrey et al. J. D. Humphrey, Cardiovascular Solid Mechanics: Cells, Tissues, and Organs, Springer, N.Y., 2002.
- Holzapfel et al. G. A. Holzapfel, T. C. Gasser, R. W. Ogden, A new constitutive framework for arterial wall mechanics and a comparative study of material models, J. Elasticity (2000) 1-48).
- an elastic modulus of a vessel wall may be roughly estimated from a Hounsfield unit (HU) of tissue surrounding a lumen boundary. Thickness of a vessel wall may be estimated from image data and/or approximated by a theoretical relationship between vessel radius and wall thickness, e.g., assuming the thickness is 1 ⁇ 5 th or 1/10 th of the radius. Vessel wall models may represent material behavior passively or may include active behavior to model tension due to smooth muscle tone in the vessel wall. The material properties may be affected by pressure, flow, wall shear stress, wall tensile stress, and/or vasoactive drugs that may alter tension in the vessel wall (e.g., by inducing smooth muscle cell contraction or relaxation).
- HU Hounsfield unit
- steps 201 - 205 of determining a patient-specific geometrical model, a physiologic model, and a biomechanical model may all pertain to a “first state.”
- a first-state model may represent the patient's conditions when imaging was performed.
- step 207 may include defining physiologic conditions, boundary conditions, and/or material properties of a patient in a second state, other than the first state.
- physiologic conditions and boundary conditions of a patient under hyperemic conditions may be defined using a method described in U.S. Pat. No. 8,315,812 issued Nov. 20, 2012, the entire disclosure of which is hereby incorporated by reference in its entirety.
- the physiologic conditions and boundary conditions of a patient after treatment may be defined using the method described in U.S. Pat. No. 8,249,815 issued Aug. 21, 2012, the entire disclosure of which is hereby incorporated in reference in its entirety.
- changes in elastic properties of a blood vessel may be modified for a second state based on an expected response to medications (e.g., nitrates).
- a second state may include determining vasoactive response of arteries in response to a “removal” of nitrates.
- the second state may thus include vasoconstriction of arteries relative to the first state, which may closer model a patient's anatomy and/or physiology under resting conditions.
- step 207 may include determining changes in properties based on data in literature.
- an expected response of nitrates known in literature is an increase in diameters of 0% to 30%, depending on the size of a vessel and whether it is healthy or diseased.
- changes in vessel size due to administration of nitrates may be determined using machine learning methods. The data may then be used to update vessel properties for the second state of the patient.
- FIG. 5 described further herein, provides further detail on machine learning methods for determining changes in geometry with respect to various states.
- step 209 may include generating an anatomic model of the second state, based on flow and pressure conditions of the patient in a second state.
- step 209 may include updating and/or revising a patient-specific first-state model (e.g., the patient-specific anatomic model from step 201 ).
- step 209 may include simulating blood flow and pressure of the patient in the second state, using the patient-specific anatomic model and biomechanical model of the patient in the first state.
- step 209 may include simulating blood flow and pressure in the first-state model, along with boundary conditions and/or material properties associated with the second-state model. Further detail regarding step 209 is provided in FIGS. 3 and 4 .
- step 211 may include performing simulations using a model reflecting a patient's second state.
- step 211 may include performing a simulation of blood flow and pressure using the second-state model.
- step 213 may include providing and/or outputting results of the simulation in the form of a report via a computer output device.
- FIG. 3 is a block diagram of an exemplary method 300 of determining a second-state model of conditions, according to an exemplary embodiment.
- step 301 may include determining various available models and/or patient conditions.
- a second-state model may include (i) an original baseline or resting state of blood vessels (e.g., arteries) prior to administration of drugs (e.g., to improve image data), (ii) a hyperemic state subsequent to administration of a drug to increase blood flow (e.g., adenosine, papaverine, ATP, Regadenoson, etc.), (iii) a simulated exercise state, (iv) a post-treatment state, etc.
- blood vessels e.g., arteries
- drugs e.g., to improve image data
- a hyperemic state subsequent to administration of a drug to increase blood flow e.g., adenosine, papaverine, ATP, Regadenoson, etc.
- a simulated exercise state
- step 303 may include determining which of the available models is of interest. For example, step 303 may include selecting one or more of the available models as a second-state model based on user selection, inferences from input associated with the first-state model, patient information, etc.
- step 305 may include determining conditions associated with the selected model.
- physiologic condition changes may include: aortic pressure decreases, heart rate increases, vascular microcirculatory resistance decreases, healthy arteries dilating in response to flow, stenosis or segments of arteries downstream of disease reducing in size in response to pressure changes, etc.
- a response to a simulated exercise state may include the following physiologic condition changes: cardiac output increases, aortic pressure increases, heart rate increases, vascular microcirculatory resistance decreases, healthy arteries' dilation in response to flow, stenosis or segments of arteries downstream of disease reducing in size in response to pressure changes, etc.
- a post-treatment state for example subsequent to treatment including angioplasty and stenting or bypass surgery, local blood pressure and flow along an arterial tree may be altered for resting conditions and high flow conditions (e.g., hyperemia, exercise, etc.).
- step 307 may include computing forces on blood vessel walls for the second state.
- step 307 may include simulating blood flow and pressure in the first-state model and using results of the simulation to modify physiologic boundary conditions to represent those of the second state.
- step 307 may be performed using, for example, (i) a reduced order model (e.g., a lumped-parameter or one-dimensional wave propagation model), (ii) a three-dimensional finite element, finite volume, lattice Boltzman, level set, immersed boundary, or particle-based method to solve 3-D equations of blood flow and pressure, or (iii) a fluid-structure interaction method to solve for blood flow, pressure, and vessel wall motion.
- a reduced order model e.g., a lumped-parameter or one-dimensional wave propagation model
- a three-dimensional finite element, finite volume, lattice Boltzman, level set, immersed boundary, or particle-based method to solve 3-D equations of blood flow and pressure or
- FIG. 4 is a block diagram of an exemplary method 400 of determining an updated geometric model based on the second-state conditions, according to an exemplary embodiment.
- method 400 may be directed at determining a geometric model based on a biomechanical model of a patient's arteries (e.g., a model from method 300 ).
- step 401 may include determining a relationship between geometry and biomechanical properties.
- step 401 may include determining one or more one-dimensional elastic and/or viscoelastic models of blood vessels relating pressure to vessel cross-sectional area along the length of a vessel.
- a representative pressure diameter curve may be calibrated to match pre-treatment pressure-diameter values at different centerline points.
- step 401 may include solving stress-equilibrium equations for a computational model of the vessel wall, with the pressure difference between the first state and the second state acting in the inner wall and a zero traction boundary condition acting on the outer surface of the vessel.
- step 403 may include solving the models and/or equations from step 401 to determine geometry based on the biomechanical data.
- step 403 may include solving for geometry correction iteratively along with computational fluid dynamics (CFD) (e.g., using predictor-corrector methods).
- CFD computational fluid dynamics
- Such a method of determining the geometry may be possible because changes in geometry affect flow rate and blood pressure.
- geometry may be solved for in a coupled manner using an arbitrary Lagrangian-Eulerian framework.
- machine learning methods may be used to model how cross-sectional area of vessels change locally, given a change in pressure and surrounding geometry.
- training data from other patients may be used to inform the model to predict area changes from pressures computed in method 300 .
- step 405 may include determining a deformation that may be computed to update the geometrical model.
- step 405 may include determining a minimal deformation that creates a segmentation with desired cross-sectional area at each location, that may then be computed to update a geometry.
- the flow domain and vessel walls may be represented by an explicit mesh. This explicit mesh may be modified by a variety of elastic deformation techniques.
- a flow domain may have an implicit representation deformed by a speed function using a level set method.
- a level set method may permit tracking shapes by building a surface from two-dimensional boundaries of shapes, where the shapes may include level “slices” of the surface.
- a speed function may be used to change a representation for a level set method defined by computed desired cross sectional areas along the centerline.
- the speed function for a level set method may include terms to control the curvature of the implicit surface as it is modified from the first state to the second state.
- Step 407 may include producing a patient-specific anatomic model to represent a patient in second state conditions. Producing the second-state model may include updating a first-state geometric model.
- step 407 may include deforming an implicit representation.
- Step 407 may further include determining whether to mesh the implicit representation with other representations or use the implicit representation directly.
- Step 409 may include using either a mesh of multiple implicit representations or a single implicit representation of modified boundary conditions for display, calculations of CFD equations, or a combination thereof.
- step 409 may include performing a simulation of blood flow and pressure using the second-state patient-specific anatomic model and/or biomechanical model.
- step 409 may include providing information based on the simulation to a user, for instance, through a report or display via a computer output device.
- method 400 may include further modeling geometric changes based on post-treatment states (e.g., angioplasty, stenting, or bypass surgery).
- deforming the mesh for an anatomic model in step 407 may include accounting for a geometry of a stent or a geometry post-angioplasty.
- a simulation of step 409 may include simulating blood flow and pressure through the anatomic models built from physiologic state boundary conditions and geometry, as well as treatment-related geometry.
- results from the simulations may be output or displayed. For example, such output may include a treatment recommendation, where several simulations may be run to simulate various treatment options.
- FIG. 5 is a block diagram of an exemplary method 500 , such as machine learning methods, of determining geometry responses to different physiologic conditions, according to an exemplary embodiment.
- step 501 may include determining information of a population of patients (e.g., patient age, gender, physical conditions, height, weight, diet, family medical history, etc.).
- step 503 may include determining image and/or experimental data associated with the population of patients.
- the image and/or experimental data may characterize the population of patients as a group.
- image and/or experimental data may include data respective to each patient in the population of patients.
- step 505 may include determining or calculating a value of interest associated with the image and/or experimental data.
- a value of interest may be a measurement (e.g., material properties of a vessel wall) and/or a change in a measurement (e.g., changes in vessel size due to administration of a nitrate).
- step 505 may include computing, for each patient in a population of patients, the value of interest.
- Step 505 may further include averaging the values for an entire population of patients.
- Step 507 may then include predicting a change in geometry based on the values given by the population of patients.
- step 507 may include using the values from step 505 to model how cross-sectional area of a vessel changes locally, given change in pressure and surrounding geometry. Step 507 may then help predict area changes from pressures computed by biomechanical modeling based on physiologic conditions.
- the present disclosure includes calculating blood flow and pressure in patient-specific arterial models updated to reflect geometric and boundary condition changes.
- the changes arise from a state change subsequent a state of a patient in which imaging was performed.
- Some instances of applications for such modeling include (i) resting, exercise, or hyperemic conditions using image data obtained subsequent administration of nitrates and/or beta blockers and/or (ii) post-treatment conditions using image data obtained prior to treatment.
- the present disclosure describes the systems and methods directed to coronary arteries, but the disclosure may also apply to simulations of blood flow and pressure in any arterial tree including but not limited to the carotid, cerebral, renal, and lower extremity arteries.
Landscapes
- Engineering & Computer Science (AREA)
- Public Health (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Primary Health Care (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Apparatus For Radiation Diagnosis (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
- Media Introduction/Drainage Providing Device (AREA)
- Instructional Devices (AREA)
Abstract
Description
Claims (20)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US15/172,742 US10354759B2 (en) | 2014-03-24 | 2016-06-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201461969573P | 2014-03-24 | 2014-03-24 | |
US14/317,726 US9390232B2 (en) | 2014-03-24 | 2014-06-27 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US15/172,742 US10354759B2 (en) | 2014-03-24 | 2016-06-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/317,726 Continuation US9390232B2 (en) | 2014-03-24 | 2014-06-27 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
Publications (2)
Publication Number | Publication Date |
---|---|
US20160283688A1 US20160283688A1 (en) | 2016-09-29 |
US10354759B2 true US10354759B2 (en) | 2019-07-16 |
Family
ID=54142391
Family Applications (6)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/317,726 Active US9390232B2 (en) | 2014-03-24 | 2014-06-27 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US14/323,128 Abandoned US20150269350A1 (en) | 2014-03-24 | 2014-07-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US14/323,201 Active US9202010B2 (en) | 2014-03-24 | 2014-07-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US14/323,170 Abandoned US20150269351A1 (en) | 2014-03-24 | 2014-07-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US15/172,742 Active US10354759B2 (en) | 2014-03-24 | 2016-06-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US15/265,463 Pending US20170004280A1 (en) | 2014-03-24 | 2016-09-14 | Systems and methods for image processing for modeling changes in patient-specific blood vessel geometry and boundary conditions |
Family Applications Before (4)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/317,726 Active US9390232B2 (en) | 2014-03-24 | 2014-06-27 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US14/323,128 Abandoned US20150269350A1 (en) | 2014-03-24 | 2014-07-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US14/323,201 Active US9202010B2 (en) | 2014-03-24 | 2014-07-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US14/323,170 Abandoned US20150269351A1 (en) | 2014-03-24 | 2014-07-03 | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US15/265,463 Pending US20170004280A1 (en) | 2014-03-24 | 2016-09-14 | Systems and methods for image processing for modeling changes in patient-specific blood vessel geometry and boundary conditions |
Country Status (4)
Country | Link |
---|---|
US (6) | US9390232B2 (en) |
EP (1) | EP3123373A1 (en) |
JP (3) | JP6378777B2 (en) |
WO (1) | WO2015148401A1 (en) |
Families Citing this family (34)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP3842001B1 (en) | 2013-03-15 | 2024-05-08 | Ace Vision Group, Inc. | Systems for affecting the biomechanical properties of tissue |
WO2014182505A1 (en) | 2013-05-10 | 2014-11-13 | Stenomics, Inc. | Modeling and simulation system for optimizing prosthetic heart valve treatment |
US9092743B2 (en) | 2013-10-23 | 2015-07-28 | Stenomics, Inc. | Machine learning system for assessing heart valves and surrounding cardiovascular tracts |
US9390232B2 (en) * | 2014-03-24 | 2016-07-12 | Heartflow, Inc. | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US9386933B2 (en) * | 2014-08-29 | 2016-07-12 | Heartflow, Inc. | Systems and methods for determination of blood flow characteristics and pathologies through modeling of myocardial blood supply |
US9292659B1 (en) | 2014-10-29 | 2016-03-22 | Heartflow, Inc. | Systems and methods for vessel reactivity to guide diagnosis or treatment of cardiovascular disease |
TWI547261B (en) * | 2015-01-29 | 2016-09-01 | 原相科技股份有限公司 | Array physiological detection system and operating method thereof |
US11020013B2 (en) | 2015-01-29 | 2021-06-01 | Pixart Imaging Inc. | Array physiological detection system and device |
US11311203B2 (en) | 2015-01-29 | 2022-04-26 | Pixart Imaging Inc. | Microcirculation detection system |
EP3062248A1 (en) * | 2015-02-27 | 2016-08-31 | Pie Medical Imaging BV | Method and apparatus for quantitative flow analysis |
US11071501B2 (en) | 2015-08-14 | 2021-07-27 | Elucid Bioiwaging Inc. | Quantitative imaging for determining time to adverse event (TTE) |
US10176408B2 (en) | 2015-08-14 | 2019-01-08 | Elucid Bioimaging Inc. | Systems and methods for analyzing pathologies utilizing quantitative imaging |
US11113812B2 (en) | 2015-08-14 | 2021-09-07 | Elucid Bioimaging Inc. | Quantitative imaging for detecting vulnerable plaque |
US11676359B2 (en) | 2015-08-14 | 2023-06-13 | Elucid Bioimaging Inc. | Non-invasive quantitative imaging biomarkers of atherosclerotic plaque biology |
US11094058B2 (en) | 2015-08-14 | 2021-08-17 | Elucid Bioimaging Inc. | Systems and method for computer-aided phenotyping (CAP) using radiologic images |
US12026868B2 (en) | 2015-08-14 | 2024-07-02 | Elucid Bioimaging Inc. | Quantitative imaging for detecting histopathologically defined plaque erosion non-invasively |
US12008751B2 (en) | 2015-08-14 | 2024-06-11 | Elucid Bioimaging Inc. | Quantitative imaging for detecting histopathologically defined plaque fissure non-invasively |
US11087459B2 (en) | 2015-08-14 | 2021-08-10 | Elucid Bioimaging Inc. | Quantitative imaging for fractional flow reserve (FFR) |
JP6829262B2 (en) | 2016-02-26 | 2021-02-10 | ハートフロー, インコーポレイテッド | Systems and methods for identifying and modeling unresolved vessels in an image-based patient-specific hemodynamic model |
US9824492B2 (en) | 2016-03-24 | 2017-11-21 | Vital Images, Inc. | Hollow object model visualization in medical images |
JP6669361B2 (en) * | 2016-04-12 | 2020-03-18 | Necソリューションイノベータ株式会社 | Blood flow analysis system, analysis request reception system, blood flow analysis method and program |
WO2018005888A1 (en) * | 2016-06-29 | 2018-01-04 | Ace Vision Group, Inc. | 3-dimensional model creation using whole eye finite element modeling of human ocular structures |
WO2018050806A1 (en) * | 2016-09-16 | 2018-03-22 | Koninklijke Philips N.V. | Apparatus and method for determining a fractional flow reserve |
US11504019B2 (en) | 2016-09-20 | 2022-11-22 | Heartflow, Inc. | Systems and methods for monitoring and updating blood flow calculations with user-specific anatomic and physiologic sensor data |
WO2018133118A1 (en) | 2017-01-23 | 2018-07-26 | 上海联影医疗科技有限公司 | System and method for analyzing blood flow state |
US11195278B2 (en) | 2017-04-06 | 2021-12-07 | Koninklijke Philips N.V. | Fractional flow reserve simulation parameter customization, calibration and/or training |
EP3691531A4 (en) | 2017-10-06 | 2021-05-26 | Emory University | Methods and systems for determining hemodynamic information for one or more arterial segments |
US11871995B2 (en) | 2017-12-18 | 2024-01-16 | Hemolens Diagnostics Sp. Z O.O. | Patient-specific modeling of hemodynamic parameters in coronary arteries |
JP7250435B2 (en) * | 2018-05-21 | 2023-04-03 | キヤノンメディカルシステムズ株式会社 | Device treatment support device, program, method and system |
EP3628652A1 (en) * | 2018-09-28 | 2020-04-01 | Eberhard Karls Universität Tübingen | Method for the stereoisomerization of chiral compounds |
CN113439287A (en) | 2019-08-05 | 2021-09-24 | 易鲁希德生物成像公司 | Combined assessment of morphological and perivascular disease markers |
AU2021207530A1 (en) * | 2020-01-16 | 2022-08-25 | Rochester Institute Of Technology | Noninvasive diagnostics of proximal heart health biomarkers |
CN112971979B (en) * | 2021-02-03 | 2022-08-16 | 上海友脉科技有限责任公司 | Simulation system, simulation method and device |
CN114587587B (en) * | 2022-04-02 | 2022-10-14 | 哈尔滨理工大学 | Foreign matter basket clamping and taking method |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20100017171A1 (en) | 2008-07-21 | 2010-01-21 | Ryan Leonard Spilker | Method for tuning patient-specific cardiovascular simulations |
US20120041739A1 (en) | 2010-08-12 | 2012-02-16 | Heartflow, Inc. | Method and System for Patient-Specific Modeling of Blood Flow |
US20120053918A1 (en) | 2010-08-12 | 2012-03-01 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
US20120084064A1 (en) | 2010-09-29 | 2012-04-05 | Nutech Ventures, Inc. | Model-based systems and methods for analyzing and predicting outcomes of vascular interventions and reconstructions |
US20120232386A1 (en) | 2011-03-09 | 2012-09-13 | Siemens Corporation | Valve treatment simulation from medical diagnostic imaging data |
WO2013071219A1 (en) | 2011-11-10 | 2013-05-16 | Siemens Corporation | Method and system for multi-scale anatomical and functional modeling of coronary circulation |
US8521556B2 (en) * | 2007-12-18 | 2013-08-27 | Koninklijke Philips N.V. | Integration of physiological models in medical decision support systems |
US8682626B2 (en) * | 2010-07-21 | 2014-03-25 | Siemens Aktiengesellschaft | Method and system for comprehensive patient-specific modeling of the heart |
Family Cites Families (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1746558B1 (en) * | 2005-07-20 | 2013-07-17 | MedTAG Ltd. | A system for simulating a manual interventional operation by a user in a medical procedure |
KR20140082663A (en) * | 2011-08-26 | 2014-07-02 | 이비엠 가부시키가이샤 | System for diagnosing bloodflow characteristics, method thereof, and computer software program |
US10373700B2 (en) * | 2012-03-13 | 2019-08-06 | Siemens Healthcare Gmbh | Non-invasive functional assessment of coronary artery stenosis including simulation of hyperemia by changing resting microvascular resistance |
US10622110B2 (en) * | 2012-03-15 | 2020-04-14 | Siemens Healthcare Gmbh | Framework for personalization of coronary flow computations during rest and hyperemia |
JP5946127B2 (en) * | 2012-05-11 | 2016-07-05 | 富士通株式会社 | Simulation method, simulation apparatus, and simulation program |
US9247918B2 (en) * | 2012-07-09 | 2016-02-02 | Siemens Aktiengesellschaft | Computation of hemodynamic quantities from angiographic data |
US10433740B2 (en) * | 2012-09-12 | 2019-10-08 | Heartflow, Inc. | Systems and methods for estimating ischemia and blood flow characteristics from vessel geometry and physiology |
US10398386B2 (en) * | 2012-09-12 | 2019-09-03 | Heartflow, Inc. | Systems and methods for estimating blood flow characteristics from vessel geometry and physiology |
EP3571998B1 (en) * | 2012-09-25 | 2021-02-17 | The Johns Hopkins University | A method for estimating flow rates, pressure gradients, coronary flow reserve, and fractional flow reserve from patient specific computed tomography angiogram-based contrast distribution data |
US9858387B2 (en) * | 2013-01-15 | 2018-01-02 | CathWorks, LTD. | Vascular flow assessment |
US20140288440A1 (en) * | 2013-03-22 | 2014-09-25 | Children's Medical Center Corporation | Systems and methods for quantitative capnogram analysis |
US9629563B2 (en) * | 2013-09-04 | 2017-04-25 | Siemens Healthcare Gmbh | Method and system for functional assessment of renal artery stenosis from medical images |
US9700219B2 (en) * | 2013-10-17 | 2017-07-11 | Siemens Healthcare Gmbh | Method and system for machine learning based assessment of fractional flow reserve |
US9390232B2 (en) * | 2014-03-24 | 2016-07-12 | Heartflow, Inc. | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions |
US9292659B1 (en) * | 2014-10-29 | 2016-03-22 | Heartflow, Inc. | Systems and methods for vessel reactivity to guide diagnosis or treatment of cardiovascular disease |
WO2017187269A1 (en) * | 2016-04-29 | 2017-11-02 | Siemens Healthcare Gmbh | Enhanced personalized evaluation of coronary artery disease using an integration of multiple medical imaging techniques |
WO2018185040A1 (en) * | 2017-04-06 | 2018-10-11 | Koninklijke Philips N.V. | Standardized coronary artery disease metric |
EP3884848A1 (en) * | 2020-03-23 | 2021-09-29 | Kardiolytics Inc. | A system and a method for determining a significance of a stenosis |
-
2014
- 2014-06-27 US US14/317,726 patent/US9390232B2/en active Active
- 2014-07-03 US US14/323,128 patent/US20150269350A1/en not_active Abandoned
- 2014-07-03 US US14/323,201 patent/US9202010B2/en active Active
- 2014-07-03 US US14/323,170 patent/US20150269351A1/en not_active Abandoned
-
2015
- 2015-03-23 WO PCT/US2015/022062 patent/WO2015148401A1/en active Application Filing
- 2015-03-23 EP EP15716919.4A patent/EP3123373A1/en not_active Withdrawn
- 2015-03-23 JP JP2016558352A patent/JP6378777B2/en active Active
-
2016
- 2016-06-03 US US15/172,742 patent/US10354759B2/en active Active
- 2016-09-14 US US15/265,463 patent/US20170004280A1/en active Pending
-
2018
- 2018-07-27 JP JP2018141442A patent/JP6665240B2/en active Active
-
2020
- 2020-02-19 JP JP2020026113A patent/JP2020157047A/en active Pending
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8521556B2 (en) * | 2007-12-18 | 2013-08-27 | Koninklijke Philips N.V. | Integration of physiological models in medical decision support systems |
US20100017171A1 (en) | 2008-07-21 | 2010-01-21 | Ryan Leonard Spilker | Method for tuning patient-specific cardiovascular simulations |
US8682626B2 (en) * | 2010-07-21 | 2014-03-25 | Siemens Aktiengesellschaft | Method and system for comprehensive patient-specific modeling of the heart |
US20120041739A1 (en) | 2010-08-12 | 2012-02-16 | Heartflow, Inc. | Method and System for Patient-Specific Modeling of Blood Flow |
US20120041320A1 (en) | 2010-08-12 | 2012-02-16 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
US20120053918A1 (en) | 2010-08-12 | 2012-03-01 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
US8249815B2 (en) | 2010-08-12 | 2012-08-21 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
US8315812B2 (en) | 2010-08-12 | 2012-11-20 | Heartflow, Inc. | Method and system for patient-specific modeling of blood flow |
US20120084064A1 (en) | 2010-09-29 | 2012-04-05 | Nutech Ventures, Inc. | Model-based systems and methods for analyzing and predicting outcomes of vascular interventions and reconstructions |
US20120232386A1 (en) | 2011-03-09 | 2012-09-13 | Siemens Corporation | Valve treatment simulation from medical diagnostic imaging data |
WO2013071219A1 (en) | 2011-11-10 | 2013-05-16 | Siemens Corporation | Method and system for multi-scale anatomical and functional modeling of coronary circulation |
Non-Patent Citations (28)
Title |
---|
Abbara S, Arbab-Zadeh A, Callister TQ, et al. SCCT guidelines for performance of coronary computed tomographic angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr 2009;3:190-204.). |
Andro Mikelic et al.: Fluid-Structure Interaction in Blood Flow, MSRI; The Legacy of Ladyzhenskaya and Oleinik: Women in Mathematics: May 18-20, 2006, pp. 11-16. |
Chabiniok et al.: Validation of a biomechanical heart model using animal data with acute myocardial infarction; CI2BM09-MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modeling; 2009; 9 pages. |
Chabiniok et al.: Validation of a biomechanical heart model using animal data with acute myocardial infarction; CI2BM09—MICCAI Workshop on Cardiovascular Interventional Imaging and Biophysical Modeling; 2009; 9 pages. |
Daniela Valdez-Jasso; Linear and Nonlinear Viscoelastic Modeling of Aorta and Carotid Pressure-Area Dynamics under in vivo and ex vivo Conditions; Published in final edited from as: Ann Biomed. Eng. May 2011; 39(5): 1438-1456. |
Emilie Marchandise et al.; A Numerical Hemodynamic Tool for Predictive Vascular Surgery, Oct. 5, 2007, Medical Engineering & Physics, pp. 1-21. |
Figueroa et al. (C.A. Figueroa, I.E. Vignon-Clementel, K.C. Jansen, T.J.R. Hughes, C.A. Taylor (2006) A Coupled Momentum Method for Modeling Blood Flow in Three-Dimensional Deformable Arteries. Computer Methods in Applied Mechanics and Engineering, vol. 195, Issues 41-43, pp. 5685-5706.). |
Figueroa et al. (C.A. Figueroa, S. Baek, C.A. Taylor, J.D. Humphrey (2009) A Computational Framework for Coupled Fluid-Solid Growth Modeling in Cardiovascular Simulations. Computer Methods in Applied Mechanics and Engineering, vol. 198, No. 45-46, pp. 3583-3602.). |
Fridez et al.: Model of Geometrical and Smooth Muscle Tone Adaptation of Carotid Artery Subject to Step change in Presssure; Am J Physiol Heart Circ Physiol; 280: H2752-2760, 2001. |
Gee et al (Gee MW, Förster C, Wall WA (2010) A computational strategy for prestressing patient-specific biomechanical problems under finite deformation. Int J Numer Methods Biomed Eng 26(1):52-72). |
Gerbeau et al. (Gerbeau J-F, Vidrascu M, Frey P (2005) Fluid-structure interaction in blood flows on geometries based on medical imaging. Comput Struct 83(2-3):155-165). |
In Olufsen et al. (Olufsen MS. Structured tree outflow condition for blood flow in larger systemic arteries. Am J Physiol Heart Circ Physiol 276:H257-H268, 1999). |
International Search Report and Written Opinion for corresponding Application No. PCT/US2015/022062 dated Jul. 2, 2015 (9 pages). |
Kioussis et al (Kiousis DE, Gasser TC, Holzapfel GA. 2007. A numerical model to study the interaction of vascular stents with human atherosclerotic lesions. Ann. Biomed. Eng. 35:1857-69). |
Larrabide et al.: GIMIAS: An Open Source Framework for Efficient Development of Research Tools and Clinical Prototypes; FIMH 2009, LNCS 5528; Springer-Verlag; pp. 417-426, 2009. |
Mann et al.: Mechanisms and Models in Heart Failure: The Biomechanical Model and Beyond; Circulation; (published by the American Heart Association); 2005; pp. 2837-2849 914 pages total). |
Marchesseau and Delingette et al.: Fast Parameter Calibration of a Cardiac Electromechanical Model From Medical Images Based on the Unscented Transform; Biomech Model Mechanobiol (2013) 12:815-831. |
Marchesseau et al.: Cardiac Mechanical Parameter Calibration Based on the Unscented Transform; N. Ayache et al. (Eds.): MICCAI 2012, Part II, LNCS 7511, Springer-Verlag; pp. 41-48, 2012; pp. 41-48. |
Moireau et al.: Cardiac Motion Extraction From Images by Filtering Estimation Based on a Biomechanical Model; FIMH 2009, LNCS 5528; Springer-Verlag; 2009; pp. 220-228. |
Raghu et al. (R. Raghu, I.E. Vignon-Clementel, C.A. Figueroa, C.A. Taylor (2011) Comparative Study of Viscoelastic Arterial Wall Models in Nonlinear One-dimensional Finite Element Simulations of Blood Flow. Journal of Biomechanical Engineering, vol. 133, No. 8, pp. 081003.). |
Sermesant et al.: Biomechanical Model Construction from Different Modalities: Application to Cardiac Images; LNCS 2488; Springer-Verlag; pp. 714-721, 2002. |
Sermesant et al.: Personalized Computational Models of the Heart for Cardiac Resynchronization Therapy; Chapter 10, Patient-Specific Modeling of the Cardiovascular System: Technology-Driven Personalized Medicine; 2010; pp. 167-182. |
Spilker and Taylor (inventor): Tuning Multidomain Hemodynamic simulations to Match Physiological Measurement; Annals of Biomedical Engineering, vol. 38, No. 8, Aug. 2010 (2010) pp. 2635-2648. |
Stergiou et al.: Baseline Measures are Altered in Biomechanical Studies; Journal of Biomechanics 38 (2005) 175-178. |
Suncica Canic, et al. Self-Consistent Effective Equations Modeling Blood Flow in Medium-To-Large Compliant Arteries: Fluid Structure-Interaction in Hemodynamics, pp. 1-38, 2005. |
Tang et al. (and inventor): Abdominal Aortic Hemodynamics in Young Healthy Adults at rest and during lower limb exercise: quantification using image-based computer modeling; Am J. Phsiol Heart Circ. Physiol 291: H668-H676, 2006. |
Taylor et al. (inventor): Patient-Specific Modeling of Cardiovascular Mechanics; Ann. Rev. Biomed. Eng. 2009. 11:109-136. |
Wan et al. (J. Wan, B.N. Steele, S.A. Spicer, S. Strohband, G.R. Feijoo, T.J.R. Hughes, C.A. Taylor (2002) A One-dimensional Finite Element Method for Simulation-Based Medical Planning for Cardiovascular Disease. Computer Methods in Biomechanics and Biomedical Engineering. vol. 5, No. 3, pp. 195-206.). |
Also Published As
Publication number | Publication date |
---|---|
US20160283688A1 (en) | 2016-09-29 |
EP3123373A1 (en) | 2017-02-01 |
US9390232B2 (en) | 2016-07-12 |
JP2020157047A (en) | 2020-10-01 |
WO2015148401A8 (en) | 2016-12-01 |
US20150269351A1 (en) | 2015-09-24 |
US20150269350A1 (en) | 2015-09-24 |
US9202010B2 (en) | 2015-12-01 |
JP6665240B2 (en) | 2020-03-13 |
JP6378777B2 (en) | 2018-08-22 |
JP2017510350A (en) | 2017-04-13 |
US20150269349A1 (en) | 2015-09-24 |
US20170004280A1 (en) | 2017-01-05 |
US20150269352A1 (en) | 2015-09-24 |
WO2015148401A1 (en) | 2015-10-01 |
JP2019005592A (en) | 2019-01-17 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US10354759B2 (en) | Systems and methods for modeling changes in patient-specific blood vessel geometry and boundary conditions | |
US11540931B2 (en) | Systems and methods for identifying personalized vascular implants from patient-specific anatomic data | |
US20220110690A1 (en) | Systems and methods for risk assessment and treatment planning of arterio-venous malformation | |
JP7416617B2 (en) | Systems and methods for diagnosing and evaluating cardiovascular disease by comparing arterial supply capacity and end organ requirements | |
KR102336929B1 (en) | Method and system for determining treatments by modifying patient-specific geometrical models | |
JP6661613B2 (en) | System and method for automatically determining myocardial bridge and effect on a patient |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: HEARTFLOW, INC., CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:TAYLOR, CHARLES A.;KIM, HYUN JIN;SANKARAN, SETHURAMAN;AND OTHERS;SIGNING DATES FROM 20140619 TO 20140625;REEL/FRAME:038979/0810 |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT RECEIVED |
|
STPP | Information on status: patent application and granting procedure in general |
Free format text: PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED |
|
STCF | Information on status: patent grant |
Free format text: PATENTED CASE |
|
AS | Assignment |
Owner name: HAYFIN SERVICES LLP, UNITED KINGDOM Free format text: SECURITY INTEREST;ASSIGNOR:HEARTFLOW, INC.;REEL/FRAME:055037/0890 Effective date: 20210119 |
|
MAFP | Maintenance fee payment |
Free format text: PAYMENT OF MAINTENANCE FEE, 4TH YR, SMALL ENTITY (ORIGINAL EVENT CODE: M2551); ENTITY STATUS OF PATENT OWNER: SMALL ENTITY Year of fee payment: 4 |
|
AS | Assignment |
Owner name: HAYFIN SERVICES LLP, UNITED KINGDOM Free format text: SECURITY INTEREST;ASSIGNOR:HEARTFLOW, INC.;REEL/FRAME:067775/0966 Effective date: 20240614 |
|
AS | Assignment |
Owner name: HEARTFLOW, INC., CALIFORNIA Free format text: RELEASE BY SECURED PARTY;ASSIGNOR:HAYFIN SERVICES LLP;REEL/FRAME:067801/0032 Effective date: 20240614 |